{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Getting Started With DNNC.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5bndlKgejLGt",
        "colab_type": "text"
      },
      "source": [
        "##Install deepC dnn Compiler\n",
        "<img src=\"https://raw.githubusercontent.com/ai-techsystems/dnnCompiler/master/misc/dnnCompilerLogo.jpg\" alt=\"DNNC\" width=\"300\"/>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkoFTHMCfpWk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install --upgrade --force-reinstall deepC==0.11"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "scvwzKb7jPqk",
        "colab_type": "text"
      },
      "source": [
        "##Import deepC.dnnc"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFZMNpFlWtVo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import deepC.dnnc as dc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5zXCTRayWpV1",
        "colab_type": "text"
      },
      "source": [
        "# Create Tensors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n26azmGHXAtj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "711d9ec7-46d2-40fd-df4e-1360c717fec4"
      },
      "source": [
        "t0 = dc.zeros(3,2)\n",
        "t1 = dc.ones(2,3)\n",
        "t2 = dc.array([[1,2,3],[4,5,6]])\n",
        "t3 = dc.array([[10,20,30],[40,50,60]]).asTypeInt()\n",
        "print(t2)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.000000 2.000000 3.000000]\n",
            " [4.000000 5.000000 6.000000]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLorujv8XB7f",
        "colab_type": "text"
      },
      "source": [
        "# Perform Pythonic Operations\n",
        "\n",
        "All python operators are available on tensors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8nRWKsrqdS66",
        "colab_type": "code",
        "outputId": "a749564b-ebc2-4303-a8ff-3512cc2d9edc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "y=t1 + t2\n",
        "print(y)\n",
        "\n",
        "print(\".\"*30)\n",
        "\n",
        "z= t3/y.asTypeInt()\n",
        "print(z)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[2.000000 3.000000 4.000000]\n",
            " [5.000000 6.000000 7.000000]]\n",
            "..............................\n",
            "[[5.000000 6.666667 7.500000]\n",
            " [8.000000 8.333333 8.571428]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yhdmtuvXPpp",
        "colab_type": "text"
      },
      "source": [
        "# Broadcasting and Implicit Conversion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y--CTjk3EFqP",
        "colab_type": "code",
        "outputId": "c5ef74bc-e12f-4d8f-d338-d4a8cd2d99cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "a=True\n",
        "b=dc.array([[1,2,3],[4,5,6]])\n",
        "y=a+b; # adding bool and floatTensor of different ranks\n",
        "y"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2.000000 3.000000 4.000000]\n",
              " [5.000000 6.000000 7.000000]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nULC3_DVXZC4",
        "colab_type": "text"
      },
      "source": [
        "# Tensor to other types\n",
        "\n",
        "1. Numpy array\n",
        "1. Python List\n",
        "1. Python Tuple\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHjOOOehXZsf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "py_list = list(y);   # convert tensor to python list\n",
        "py_tuple = tuple(y); # convert tensor to python tuple\n",
        "np_ary = y.numpy()   # convert tensor to numpy array"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7sofLO-bXnbf",
        "colab_type": "text"
      },
      "source": [
        "# Looping throught Tensors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bI-gd2PzXn7B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "4d4b5d1b-ba7e-4c7e-97d0-b8a1d77562be"
      },
      "source": [
        "for num in dc.arange(5):\n",
        "  print(num)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0\n",
            "1.0\n",
            "2.0\n",
            "3.0\n",
            "4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zSzceV8XqCK",
        "colab_type": "text"
      },
      "source": [
        "# Neural Network Operators\n",
        "\n",
        "100s of NN operators are available"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i60iuPTxYN0j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#x=dc.arange(4,-4)\n",
        "#x=dc.array([-4,-3,-2,-1,0,1,2,3,4])\n",
        "#y=dc.sigmoid(x)\n",
        "\n",
        "x=dc.arange(1256)/100\n",
        "sinx=dc.sin(x)\n",
        "cosx=dc.cos(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nP3frTpxYTHI",
        "colab_type": "text"
      },
      "source": [
        "# Plot Data\n",
        "DNNC tensors support all popular libraries inlcuding matplot, seaborn etc."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9w9EwgOJYdYW",
        "colab_type": "text"
      },
      "source": [
        "## Line Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-zeo3RXYkqQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "8329ec52-6e38-4346-c6f8-22f5255c343e"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot (x.data(),sinx.data())\n",
        "plt.plot(x.data(), cosx.data())"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb5cb2349b0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CBCcRSZC4SLtZZ5C/H+uy4maPYFxxAIQfpG8w2hKX0Ovp0ISjZWpaO6Vlo6NJ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\nKQGE//Y4dAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P7EbspYJYhMy",
        "colab_type": "text"
      },
      "source": [
        "## Histogram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhaKOxUYYlyY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "1ce54b0d-b480-4e50-b232-789aff499ad7"
      },
      "source": [
        "plt.hist(list(dc.array(10000)), bins=50)\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+lH4gyVyST7QPqMdVz0eTnE7y6FDbgscrA7e0Oo8m2TOB2n69/dseTfKpJJuHtn2g1faV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\n/j8h2OcTDAIFlwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bO1vxm60YoR6",
        "colab_type": "text"
      },
      "source": [
        "# Scatter Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MCGVkbMjYrnv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "e056c803-8b0c-491b-c42f-446d78e59cd4"
      },
      "source": [
        "plt.scatter(dc.array(256).data(), \n",
        "            list(dc.sin(dc.array(256))),\n",
        "            c=np.random.rand(256),\n",
        "            alpha=0.5)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fb5cabf7f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qcLEi/KskS12kSj0Tk+7Kqn0Js4uFlAWEmNqGUVHcREQOn/+2eZDp5mFwJEUi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BQo5opcPNVTUV8A4Ro/y9e0kh/dgFvfhNuxBtXcjEtfxYZtldeakFLiW1NPqtnH20fPs3ffWfyN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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9OM7rNr6YuYz",
        "colab_type": "text"
      },
      "source": [
        "#REFERENCES\n",
        "\n",
        "1. [Getting Started with DNNC](https://medium.com/@srohit0/getting-started-with-dnnc-620d531d7cf3)\n",
        "1. [Open Source Github Repository](bit.ly/dnnCompiler)"
      ]
    }
  ]
}
